03 diff expression Jaeger/Jessberger
Izaskun Mallona (Mark Robinson lab)
Context
Jaeger chez Jessberger provided a Seurat object with clustering results/genotypes/batch effects and asked for a differential expression analysis with batch as blocking factors (Friday 16th Aug 2019).
To be more precise, Ascl1_5day_2 and Ascl1_12wk_2 show batch effects they would like to subtract. Analysis are run using edgeR’s glmQLFit with batch as blocking factor; and logistic regression (LR) with batch as latent variables.
Ascl vs Gli comparisons are relevant biologically; days (5d, 12 week etc) are unimportant.
Only genes with at least TPM above 1 in more than 25% of the cells are included into the edgeR glmQLFit to avoid too many false positives for lowly expressed genes.
Only genes represented in 20% of the cells of at least one group are included into the logistic regression analysis.
Datatables can be downloaded to be browsed using R or spreadsheet software.
Some of the markers are plotted as ridgeplots, but the comprehensive list of significant results (adj pvalue < 0.05) can be browsed/downloaded as datatables.
P-value adjustment Bonferroni for LR tests, FDR for edgeR’s glmQLFit.
Last results are cluster-wise, e.g. cells are compared according to both genotype (Ascl or Gli) and cluster (MN, IN, etc; annotated by Jessberger people) they belong to. We provide two analysis for that:
-First markers that are specific for each one of the genotype:cluster combination (that allows to distinguish them from the others). Instead of comparing Ascl vs Gli1 in cluster 1, then in cluster 2 etc what I did was to get the markers that distinguish Ascl:cluster1 from everything else, Ascl:cluster2 from everything else, etc. Thus keeping the ‘background’ in all comparisons. - Second, genotypes comparison within clusters (only mn_ascl vs mn_gli, in_ascl vs in_gli etc).
Load
analysis_tag <- '03_diff_expression'library(pheatmap)
library(knitr)
library(scater)
library(Seurat)
library(Cairo)
library(edgeR)
library(data.table)
## library(biomaRt)
library(dplyr)
## library(reshape2)
#library(iSEE)
## library(Matrix)
## library(scran)
## library(mvoutlier)
## library(shiny)
## library(kableExtra)
## library(velocyto.R)
## library(cowplot)
## library(corrplot)
library(DT)
library(devtools)
## library(viridis)
## library("CellMixS")
library(gplots)
library(pheatmap)ac <- function(col, alpha = 1) {
apply(sapply(col, col2rgb)/255, 2, function(x) rgb(x[1],
x[2], x[3], alpha = alpha))
}
##' subsets a dge to cells annotated as `genotype` in column
## ' `clusterwise`; 25% cells with over 1 TPM to be returned
##'
##'
##' @title dgelist subset
##' @param dge dgelist
##' @param genotypes charvector
##' @param tpm_mat tpm matrix
##' @return a dgelist object
##' @author izaskun.mallona@gmail.com
subset_cells <- function(dge, genotypes, tpm_mat) {
mod <- dge
cells <- rownames(dge$samples[dge$samples$clusterwise %in%
genotypes, ])
keep <- rownames(tpm_mat)[rowSums(tpm_mat[, cells] > 1) >=
0.25 * ncol(dge$counts[, cells])]
mod$counts <- dge$counts[keep, cells]
mod$samples <- dge$samples[cells, ]
return(mod)
}Data load
Provided by Jaeger
d <- readRDS(file.path("..", "data", "Seurat_object_Gli_Ascl_combi_meta.Robj"))Checking metadata
table(d@meta.data$group, d@meta.data$cluster)
ndNSC dNSC IN MN
Ascl1_5day 35 109 172 185
Ascl1_12wk 30 26 65 112
Gli1_5day 63 25 19 28
Gli1_12wk 20 19 37 110table(d@meta.data$group, d@meta.data$batch_group)
Ascl1_12wk_1 Ascl1_12wk_2 Ascl1_5day_1 Ascl1_5day_2 Gli1_12wk
Ascl1_5day 0 0 264 237 0
Ascl1_12wk 57 176 0 0 0
Gli1_5day 0 0 0 0 0
Gli1_12wk 0 0 0 0 186
Gli1_5day_1 Gli1_5day_2
Ascl1_5day 0 0
Ascl1_12wk 0 0
Gli1_5day 55 80
Gli1_12wk 0 0Diff expression All cells (all clusters) Gli1 vs Ascl1
edgeR DGEList object (full data)
dge_full <- DGEList(counts = as.matrix(GetAssayData(d@assays$RNA,
slot = "counts")), samples = d@meta.data)Annotating genes (Asia biomart mirror because european was down while rendering)
tmp <- list()
tmp$ids = rownames(dge_full$counts)
tmp$filters = "mgi_symbol"
tmp$attributes = c(tmp$filters, "ensembl_gene_id", "chromosome_name",
"gene_biotype", "start_position", "end_position")
tmp$biomart = "ENSEMBL_MART_ENSEMBL"
tmp$dataset = "mmusculus_gene_ensembl"
tmp$host = "asia.ensembl.org"
tmp$bmart <- biomaRt::useMart(biomart = tmp$biomart, dataset = tmp$dataset,
host = tmp$host)
feature_info <- biomaRt::getBM(attributes = tmp$attributes, filters = tmp$filters,
values = tmp$ids, mart = tmp$bmart)Batch submitting query [=>------------------------] 6% eta: 18s
Batch submitting query [=>------------------------] 9% eta: 23s
Batch submitting query [==>-----------------------] 12% eta: 25s
Batch submitting query [===>----------------------] 16% eta: 26s
Batch submitting query [====>---------------------] 19% eta: 26s
Batch submitting query [=====>--------------------] 22% eta: 26s
Batch submitting query [=====>--------------------] 25% eta: 25s
Batch submitting query [======>-------------------] 28% eta: 24s
Batch submitting query [=======>------------------] 31% eta: 24s
Batch submitting query [========>-----------------] 34% eta: 23s
Batch submitting query [=========>----------------] 38% eta: 22s
Batch submitting query [==========>---------------] 41% eta: 21s
Batch submitting query [==========>---------------] 44% eta: 20s
Batch submitting query [===========>--------------] 47% eta: 19s
Batch submitting query [============>-------------] 50% eta: 18s
Batch submitting query [=============>------------] 53% eta: 17s
Batch submitting query [==============>-----------] 56% eta: 16s
Batch submitting query [==============>-----------] 59% eta: 15s
Batch submitting query [===============>----------] 62% eta: 14s
Batch submitting query [================>---------] 66% eta: 13s
Batch submitting query [=================>--------] 69% eta: 12s
Batch submitting query [==================>-------] 72% eta: 10s
Batch submitting query [===================>------] 75% eta: 9s
Batch submitting query [===================>------] 78% eta: 8s
Batch submitting query [====================>-----] 81% eta: 7s
Batch submitting query [=====================>----] 84% eta: 6s
Batch submitting query [======================>---] 88% eta: 5s
Batch submitting query [=======================>--] 91% eta: 4s Batch
submitting query [=======================>--] 94% eta: 2s Batch submitting
query [========================>-] 97% eta: 1s Batch submitting query
[==========================] 100% eta: 0smm <- match(tmp$ids, feature_info[[tmp$filters]])
feature_info_full <- feature_info[mm, ]
old_rdata <- data.frame(id = rownames(dge_full$counts))
keep_cols <- !(colnames(old_rdata) %in% colnames(feature_info_full))
gene_meta_data <- cbind(old_rdata[, keep_cols], feature_info_full)
rownames(gene_meta_data) <- gene_meta_data[, 1]
gene_meta_data$length <- gene_meta_data$end_position - gene_meta_data$start_position
## rm(tmp, old_rdata, keep_cols, mm, feature_info)Removing mitochondrial genes
keep <- !grepl("mt-", rownames(dge_full))
table(keep)keep
FALSE TRUE
13 15502 dge_full <- dge_full[keep, ]DefaultAssay(d) <- "RNA"
keep <- !grepl("mt-", rownames(d@assays$RNA))
table(keep)keep
FALSE TRUE
13 15502 d <- d[keep, ]Generating unexact TPMs from raw counts (because we take gene lengths and didn’t quantify isoforms)
## removing NAs
gene_meta_data <- na.omit(gene_meta_data)
genes <- intersect(rownames(dge_full$counts), rownames(gene_meta_data))
dge_full$counts <- dge_full$counts[genes, ]
x <- dge_full$counts/gene_meta_data[rownames(dge_full$counts),
"length"]
tpm_mat <- t(t(x) * 1e+06/colSums(x))Filtering in only genes with an estimated TPM above 1 in more than 25% of the cells, as in https://www.nature.com/articles/nmeth.4612. TPMs because of the sequencing length bias https://f1000research.com/articles/6-595.
dge <- dge_full
keep <- rowSums(tpm_mat > 1) >= 0.25 * ncol(dge$counts)
table(keep)keep
FALSE TRUE
10235 4895 dge <- dge[keep, ]batch <- dge$samples$batch_group
genotype <- dge$samples$type
cluster <- dge$samples$clusterBatch and cluster are full rank, trying to split the batch into a meaningful design
alt_batch <- as.character(batch)
alt_batch[alt_batch == "Ascl1_12wk_1"] <- "B1"
alt_batch[alt_batch == "Ascl1_12wk_2"] <- "B2"
alt_batch[alt_batch == "Ascl1_5day_1"] <- "B1"
alt_batch[alt_batch == "Ascl1_5day_2"] <- "B2"
alt_batch[alt_batch == "Gli1_12wk"] <- "B1"
alt_batch[alt_batch == "Gli1_5day_1"] <- "B1"
alt_batch[alt_batch == "Gli1_5day_2"] <- "B1"
alt_batch <- as.factor(alt_batch)
table(alt_batch)alt_batch
B1 B2
642 413 design <- model.matrix(~0 + genotype + alt_batch)# norm factors
dge <- calcNormFactors(dge)
# Fit the NB GLMs with QL methods
dge <- estimateDisp(dge, design)
fit <- glmQLFit(dge, design)limma::plotMDS(dge, col = as.numeric(as.factor(dge$samples$type)),
pch = 19)
plotBCV(dge)
plotQLDisp(fit)
Comparing Gli and Ascl genotypes with glmQLF
avsgli <- makeContrasts(genotypeAscl1 - genotypeGli1, levels = design)
qlf_genot <- glmQLFTest(fit, contrast = avsgli)tt_genot <- topTags(qlf_genot, adjust.method = "BH", sort.by = "PValue",
n = 1e+06, p.value = 0.05)
tt_genot_df <- data.frame(gene = rownames(tt_genot), tt_genot)
tt_genot_df$analysis <- "ascl_vs_gli_glmqlf"
DT::datatable(tt_genot_df %>% as.data.frame() %>% dplyr::mutate_if(is.numeric,
funs(round(., 2))), extensions = c("Buttons", "FixedColumns"),
rownames = FALSE, options = list(dom = "Bfrtip", scrollX = TRUE,
fixedColumns = list(leftColumns = 1), buttons = c("csv",
"excel")))Warning: funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once per session.hist(tt_genot$table$PValue, 50)
hist(tt_genot$table$FDR, 50)
plotSmear(qlf_genot)
Heat map of top 50 differentially expressed genes, logCPM as in https://f1000research.com/articles/5-1438
logCPM <- cpm(dge, prior.count = 2, log = TRUE)
rownames(logCPM) <- rownames(dge$counts)
colnames(logCPM) <- rownames(dge$samples)
## table is already sorted
o <- head(rownames(tt_genot$table), 50)
logCPM <- logCPM[o, ]
logCPM <- t(scale(t(logCPM)))
col.pan <- colorpanel(100, "blue", "white", "red")
## sampling only 100 cells
set.seed(1)
logCPM <- logCPM[, sample(1:ncol(logCPM), 100)]
pheatmap(logCPM, color = col.pan, annotation_col = dge$samples[,
c("cluster", "batch_group", "type")])DefaultAssay(d) <- "RNA"
Idents(d) <- d@meta.data$combi
RidgePlot(d, head(tt_genot_df$gene), ncol = 2)
Comparing Gli and Ascl genotypes with logistic regression
In this case, we require 20% of the cells in either Ascl1 or Gli1 to express the gene.
Note about differential expression results: it hasthe following columns, - gene: the gene symbol - p_val : p_val (unadjusted) - avg_logFC : log fold-chage of the average expression between the two groups. Positive values indicate that the feature is more highly expressed in the first group. - pct.1 : The percentage of cells where the feature is detected in the first group - pct.2 : The percentage of cells where the feature is detected in the second group - p_val_adj : Adjusted p-value, based on bonferroni correction using all features in the dataset. - analysis: a tag describing the analysis carried out
alt_batch <- as.character(d@meta.data$batch)
alt_batch[alt_batch == "Ascl1_12wk_1"] <- "B1"
alt_batch[alt_batch == "Ascl1_12wk_2"] <- "B2"
alt_batch[alt_batch == "Ascl1_5day_1"] <- "B1"
alt_batch[alt_batch == "Ascl1_5day_2"] <- "B2"
alt_batch[alt_batch == "Gli1_12wk"] <- "B1"
alt_batch[alt_batch == "Gli1_5day_1"] <- "B1"
alt_batch[alt_batch == "Gli1_5day_2"] <- "B1"
alt_batch <- as.factor(alt_batch)
table(alt_batch)alt_batch
B1 B2
642 413 d@meta.data$alt_batch <- alt_batch
DefaultAssay(d) <- "RNA"
Idents(d) <- "type"
markers <- FindMarkers(d, ident.1 = "Ascl1", ident.2 = "Gli1",
test.use = "LR", latent.vars = "alt_batch", min.pct = 0.2)Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredmarkers <- markers[order(markers$p_val_adj), ]
markers <- markers[markers$p_val_adj < 0.05, ]
markers$analysis <- "ascl_vs_gli_lr"
markers <- data.frame(gene = rownames(markers), markers)
DT::datatable(markers %>% as.data.frame() %>% dplyr::mutate_if(is.numeric,
funs(round(., 2))), extensions = c("Buttons", "FixedColumns"),
rownames = FALSE, options = list(dom = "Bfrtip", scrollX = TRUE,
fixedColumns = list(leftColumns = 1), buttons = c("csv",
"excel")))Idents(d) <- d@meta.data$combi
RidgePlot(d, head(rownames(markers)), ncol = 2)
Comparing the overlap between LR and edgeR
edge <- data.frame(gene = rownames(tt_genot$table), tt_genot$table)
lr <- data.frame(gene = rownames(markers), markers)
merged <- merge(edge, lr, by = "gene", all = TRUE)
nrow(edge)[1] 1335nrow(lr)[1] 320length(intersect(edge$gene, lr$gene))[1] 264merged$logFDR <- log10(merged$FDR)
merged$logp_val_adj <- log10(merged$p_val_adj)
plot(merged[c("logFC", "avg_logFC", "logFDR", "logp_val_adj")])
Unsupervised genotype:cluster-wise DE analysis
Getting the markers that are characteristic of each genotype:cluster combination (as compared to the rest).
DefaultAssay(d) <- "RNA"
Idents(d) <- "combi"
markers_combi <- FindAllMarkers(d, test.use = "LR", latent.vars = "alt_batch",
min.pct = 0.2)Calculating cluster Gli1_ndNSCCalculating cluster Ascl1_ndNSCCalculating cluster Gli1_dNSCCalculating cluster Ascl1_dNSCCalculating cluster Gli1_INCalculating cluster Ascl1_INCalculating cluster Gli1_MNCalculating cluster Ascl1_MNmarkers_combi <- markers_combi[markers_combi$p_val_adj < 0.05,
]
markers_combi$analysis <- "all_markers_lr"
DT::datatable(markers_combi %>% as.data.frame() %>% dplyr::mutate_if(is.numeric,
funs(round(., 2))), extensions = c("Buttons", "FixedColumns"),
rownames = FALSE, options = list(dom = "Bfrtip", scrollX = TRUE,
fixedColumns = list(leftColumns = 1), buttons = c("csv",
"excel")))Planned genotype:cluster-wise comparisons
Finally, genotypes comparison within clusters, e.g. for cluster MN comparing cells from mn_ascl vs mn_gli; for IN cells, coparing in_ascl vs in_gli etc.
Ascl vs Gli for cluster ndNSC
DefaultAssay(d) <- "RNA"
Idents(d) <- "combi"
markers <- FindMarkers(d, ident.1 = "Ascl1_ndNSC", ident.2 = "Gli1_ndNSC",
test.use = "LR", latent.vars = "alt_batch", min.pct = 0.2)
markers <- markers[order(markers$p_val_adj), ]
markers <- markers[markers$p_val_adj < 0.05, ]
markers$analysis <- "asclndnsc_glindnsc_lr"
markers <- data.frame(gene = rownames(markers), markers)
DT::datatable(markers %>% as.data.frame() %>% dplyr::mutate_if(is.numeric,
funs(round(., 2))), extensions = c("Buttons", "FixedColumns"),
rownames = FALSE, options = list(dom = "Bfrtip", scrollX = TRUE,
fixedColumns = list(leftColumns = 1), buttons = c("csv",
"excel")))Idents(d) <- "combi"
RidgePlot(d, head(rownames(markers)), ncol = 2)
Ascl vs Gli for cluster dNSC
DefaultAssay(d) <- "RNA"
Idents(d) <- "combi"
markers <- FindMarkers(d, ident.1 = "Ascl1_dNSC", ident.2 = "Gli1_dNSC",
test.use = "LR", latent.vars = "alt_batch", min.pct = 0.2)
markers <- markers[order(markers$p_val_adj), ]
markers <- markers[markers$p_val_adj < 0.05, ]
markers$analysis <- "ascldnsc_glidnsc_lr"
markers <- data.frame(gene = rownames(markers), markers)
DT::datatable(markers %>% as.data.frame() %>% dplyr::mutate_if(is.numeric,
funs(round(., 2))), extensions = c("Buttons", "FixedColumns"),
rownames = FALSE, options = list(dom = "Bfrtip", scrollX = TRUE,
fixedColumns = list(leftColumns = 1), buttons = c("csv",
"excel")))Idents(d) <- "combi"
RidgePlot(d, head(rownames(markers)), ncol = 2)
Ascl vs Gli for cluster IN
DefaultAssay(d) <- "RNA"
Idents(d) <- "combi"
markers <- FindMarkers(d, ident.1 = "Ascl1_IN", ident.2 = "Gli1_IN",
test.use = "LR", latent.vars = "alt_batch", min.pct = 0.2)
markers <- markers[order(markers$p_val_adj), ]
markers <- markers[markers$p_val_adj < 0.05, ]
markers$analysis <- "asclin_gliin_lr"
markers <- data.frame(gene = rownames(markers), markers)
DT::datatable(markers %>% as.data.frame() %>% dplyr::mutate_if(is.numeric,
funs(round(., 2))), extensions = c("Buttons", "FixedColumns"),
rownames = FALSE, options = list(dom = "Bfrtip", scrollX = TRUE,
fixedColumns = list(leftColumns = 1), buttons = c("csv",
"excel")))Idents(d) <- "combi"
RidgePlot(d, head(rownames(markers)), ncol = 2)
Ascl vs Gli for cluster MN
DefaultAssay(d) <- "RNA"
Idents(d) <- "combi"
markers <- FindMarkers(d, ident.1 = "Ascl1_MN", ident.2 = "Gli1_MN",
test.use = "LR", latent.vars = "alt_batch", min.pct = 0.2)
markers <- markers[order(markers$p_val_adj), ]
markers <- markers[markers$p_val_adj < 0.05, ]
markers$analysis <- "asclmn_glimn_lr"
markers <- data.frame(gene = rownames(markers), markers)
DT::datatable(markers %>% as.data.frame() %>% dplyr::mutate_if(is.numeric,
funs(round(., 2))), extensions = c("Buttons", "FixedColumns"),
rownames = FALSE, options = list(dom = "Bfrtip", scrollX = TRUE,
fixedColumns = list(leftColumns = 1), buttons = c("csv",
"excel")))Idents(d) <- "combi"
RidgePlot(d, head(rownames(markers)), ncol = 2)
Session
date()[1] "Mon Aug 19 16:29:50 2019"devtools::session_info()─ Session info ──────────────────────────────────────────────────────────
setting value
version R version 3.6.1 (2019-07-05)
os Ubuntu 16.04.6 LTS
system x86_64, linux-gnu
ui X11
language en_US
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Madrid
date 2019-08-19
─ Packages ──────────────────────────────────────────────────────────────
package * version date lib source
AnnotationDbi 1.46.0 2019-05-02 [1] Bioconductor
ape 5.3 2019-03-17 [1] CRAN (R 3.6.1)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.1)
backports 1.1.4 2019-04-10 [1] CRAN (R 3.6.1)
beeswarm 0.2.3 2016-04-25 [1] CRAN (R 3.6.1)
bibtex 0.4.2 2017-06-30 [1] CRAN (R 3.6.1)
Biobase * 2.44.0 2019-05-02 [1] Bioconductor
BiocGenerics * 0.30.0 2019-05-02 [1] Bioconductor
BiocNeighbors 1.2.0 2019-05-02 [1] Bioconductor
BiocParallel * 1.18.0 2019-05-03 [1] Bioconductor
BiocSingular 1.0.0 2019-05-02 [1] Bioconductor
biomaRt 2.40.1 2019-06-27 [1] Bioconductor
bit 1.1-14 2018-05-29 [1] CRAN (R 3.6.1)
bit64 0.9-7 2017-05-08 [1] CRAN (R 3.6.1)
bitops 1.0-6 2013-08-17 [1] CRAN (R 3.6.1)
blob 1.2.0 2019-07-09 [1] CRAN (R 3.6.1)
Cairo * 1.5-10 2019-03-28 [1] CRAN (R 3.6.1)
callr 3.3.1 2019-07-18 [1] CRAN (R 3.6.1)
caTools 1.17.1.2 2019-03-06 [1] CRAN (R 3.6.1)
cli 1.1.0 2019-03-19 [1] CRAN (R 3.6.1)
cluster 2.1.0 2019-06-19 [1] CRAN (R 3.6.1)
codetools 0.2-16 2018-12-24 [1] CRAN (R 3.6.1)
colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.6.1)
cowplot 1.0.0 2019-07-11 [1] CRAN (R 3.6.1)
crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.1)
crosstalk 1.0.0 2016-12-21 [1] CRAN (R 3.6.1)
curl 3.3 2019-01-10 [1] CRAN (R 3.6.1)
data.table * 1.12.2 2019-04-07 [1] CRAN (R 3.6.1)
DBI 1.0.0 2018-05-02 [1] CRAN (R 3.6.1)
DelayedArray * 0.10.0 2019-05-02 [1] Bioconductor
DelayedMatrixStats 1.6.0 2019-05-02 [1] Bioconductor
desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.1)
devtools * 2.1.0 2019-07-06 [1] CRAN (R 3.6.1)
digest 0.6.20 2019-07-04 [1] CRAN (R 3.6.1)
dplyr * 0.8.3 2019-07-04 [1] CRAN (R 3.6.1)
DT * 0.7 2019-06-11 [1] CRAN (R 3.6.1)
edgeR * 3.26.5 2019-06-21 [1] Bioconductor
evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.1)
fitdistrplus 1.0-14 2019-01-23 [1] CRAN (R 3.6.1)
formatR 1.7 2019-06-11 [1] CRAN (R 3.6.1)
fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.1)
future 1.14.0 2019-07-02 [1] CRAN (R 3.6.1)
future.apply 1.3.0 2019-06-18 [1] CRAN (R 3.6.1)
gbRd 0.4-11 2012-10-01 [1] CRAN (R 3.6.1)
gdata 2.18.0 2017-06-06 [1] CRAN (R 3.6.1)
GenomeInfoDb * 1.20.0 2019-05-02 [1] Bioconductor
GenomeInfoDbData 1.2.1 2019-07-17 [1] Bioconductor
GenomicRanges * 1.36.0 2019-05-02 [1] Bioconductor
ggbeeswarm 0.6.0 2017-08-07 [1] CRAN (R 3.6.1)
ggplot2 * 3.2.0 2019-06-16 [1] CRAN (R 3.6.1)
ggrepel 0.8.1 2019-05-07 [1] CRAN (R 3.6.1)
ggridges 0.5.1 2018-09-27 [1] CRAN (R 3.6.1)
globals 0.12.4 2018-10-11 [1] CRAN (R 3.6.1)
glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.1)
gplots * 3.0.1.1 2019-01-27 [1] CRAN (R 3.6.1)
gridExtra 2.3 2017-09-09 [1] CRAN (R 3.6.1)
gtable 0.3.0 2019-03-25 [1] CRAN (R 3.6.1)
gtools 3.8.1 2018-06-26 [1] CRAN (R 3.6.1)
hms 0.5.0 2019-07-09 [1] CRAN (R 3.6.1)
htmltools 0.3.6 2017-04-28 [1] CRAN (R 3.6.1)
htmlwidgets 1.3 2018-09-30 [1] CRAN (R 3.6.1)
httpuv 1.5.1 2019-04-05 [1] CRAN (R 3.6.1)
httr 1.4.0 2018-12-11 [1] CRAN (R 3.6.1)
ica 1.0-2 2018-05-24 [1] CRAN (R 3.6.1)
igraph 1.2.4.1 2019-04-22 [1] CRAN (R 3.6.1)
IRanges * 2.18.1 2019-05-31 [1] Bioconductor
irlba 2.3.3 2019-02-05 [1] CRAN (R 3.6.1)
jsonlite 1.6 2018-12-07 [1] CRAN (R 3.6.1)
KernSmooth 2.23-15 2015-06-29 [1] CRAN (R 3.6.1)
knitr * 1.23 2019-05-18 [1] CRAN (R 3.6.1)
labeling 0.3 2014-08-23 [1] CRAN (R 3.6.1)
later 0.8.0 2019-02-11 [1] CRAN (R 3.6.1)
lattice 0.20-38 2018-11-04 [1] CRAN (R 3.6.1)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.6.1)
limma * 3.40.2 2019-05-17 [1] Bioconductor
listenv 0.7.0 2018-01-21 [1] CRAN (R 3.6.1)
lmtest 0.9-37 2019-04-30 [1] CRAN (R 3.6.1)
locfit 1.5-9.1 2013-04-20 [1] CRAN (R 3.6.1)
lsei 1.2-0 2017-10-23 [1] CRAN (R 3.6.1)
magrittr 1.5 2014-11-22 [1] CRAN (R 3.6.1)
MASS 7.3-51.4 2019-03-31 [1] CRAN (R 3.6.1)
Matrix 1.2-17 2019-03-22 [1] CRAN (R 3.6.1)
matrixStats * 0.54.0 2018-07-23 [1] CRAN (R 3.6.1)
memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.1)
metap 1.1 2019-02-06 [1] CRAN (R 3.6.1)
mime 0.7 2019-06-11 [1] CRAN (R 3.6.1)
munsell 0.5.0 2018-06-12 [1] CRAN (R 3.6.1)
nlme 3.1-140 2019-05-12 [1] CRAN (R 3.6.1)
npsurv 0.4-0 2017-10-14 [1] CRAN (R 3.6.1)
pbapply 1.4-1 2019-07-15 [1] CRAN (R 3.6.1)
pheatmap * 1.0.12 2019-01-04 [1] CRAN (R 3.6.1)
pillar 1.4.2 2019-06-29 [1] CRAN (R 3.6.1)
pkgbuild 1.0.4 2019-08-05 [1] CRAN (R 3.6.1)
pkgconfig 2.0.2 2018-08-16 [1] CRAN (R 3.6.1)
pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.1)
plotly 4.9.0 2019-04-10 [1] CRAN (R 3.6.1)
plyr 1.8.4 2016-06-08 [1] CRAN (R 3.6.1)
png 0.1-7 2013-12-03 [1] CRAN (R 3.6.1)
prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.6.1)
processx 3.4.1 2019-07-18 [1] CRAN (R 3.6.1)
progress 1.2.2 2019-05-16 [1] CRAN (R 3.6.1)
promises 1.0.1 2018-04-13 [1] CRAN (R 3.6.1)
ps 1.3.0 2018-12-21 [1] CRAN (R 3.6.1)
purrr 0.3.2 2019-03-15 [1] CRAN (R 3.6.1)
R.methodsS3 1.7.1 2016-02-16 [1] CRAN (R 3.6.1)
R.oo 1.22.0 2018-04-22 [1] CRAN (R 3.6.1)
R.utils 2.9.0 2019-06-13 [1] CRAN (R 3.6.1)
R6 2.4.0 2019-02-14 [1] CRAN (R 3.6.1)
RANN 2.6.1 2019-01-08 [1] CRAN (R 3.6.1)
RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 3.6.1)
Rcpp 1.0.1 2019-03-17 [1] CRAN (R 3.6.1)
RCurl 1.95-4.12 2019-03-04 [1] CRAN (R 3.6.1)
Rdpack 0.11-0 2019-04-14 [1] CRAN (R 3.6.1)
remotes 2.1.0 2019-06-24 [1] CRAN (R 3.6.1)
reshape2 1.4.3 2017-12-11 [1] CRAN (R 3.6.1)
reticulate 1.12 2019-04-12 [1] CRAN (R 3.6.1)
rlang 0.4.0 2019-06-25 [1] CRAN (R 3.6.1)
rmarkdown 1.14 2019-07-12 [1] CRAN (R 3.6.1)
ROCR 1.0-7 2015-03-26 [1] CRAN (R 3.6.1)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.1)
RSQLite 2.1.1 2018-05-06 [1] CRAN (R 3.6.1)
rsvd 1.0.1 2019-06-02 [1] CRAN (R 3.6.1)
Rtsne 0.15 2018-11-10 [1] CRAN (R 3.6.1)
S4Vectors * 0.22.0 2019-05-02 [1] Bioconductor
scales 1.0.0 2018-08-09 [1] CRAN (R 3.6.1)
scater * 1.12.2 2019-05-24 [1] Bioconductor
sctransform 0.2.0 2019-04-12 [1] CRAN (R 3.6.1)
SDMTools 1.1-221.1 2019-04-18 [1] CRAN (R 3.6.1)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.1)
Seurat * 3.0.2 2019-06-14 [1] CRAN (R 3.6.1)
shiny 1.3.2 2019-04-22 [1] CRAN (R 3.6.1)
SingleCellExperiment * 1.6.0 2019-05-02 [1] Bioconductor
stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.1)
stringr 1.4.0 2019-02-10 [1] CRAN (R 3.6.1)
SummarizedExperiment * 1.14.0 2019-05-02 [1] Bioconductor
survival 2.44-1.1 2019-04-01 [1] CRAN (R 3.6.1)
testthat 2.2.1 2019-07-25 [1] CRAN (R 3.6.1)
tibble 2.1.3 2019-06-06 [1] CRAN (R 3.6.1)
tidyr 0.8.3 2019-03-01 [1] CRAN (R 3.6.1)
tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.6.1)
tsne 0.1-3 2016-07-15 [1] CRAN (R 3.6.1)
usethis * 1.5.1 2019-07-04 [1] CRAN (R 3.6.1)
vctrs 0.2.0 2019-07-05 [1] CRAN (R 3.6.1)
vipor 0.4.5 2017-03-22 [1] CRAN (R 3.6.1)
viridis 0.5.1 2018-03-29 [1] CRAN (R 3.6.1)
viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.6.1)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.1)
xfun 0.8 2019-06-25 [1] CRAN (R 3.6.1)
XML 3.98-1.20 2019-06-06 [1] CRAN (R 3.6.1)
xtable 1.8-4 2019-04-21 [1] CRAN (R 3.6.1)
XVector 0.24.0 2019-05-02 [1] Bioconductor
yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.1)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.6.1)
zlibbioc 1.30.0 2019-05-02 [1] Bioconductor
zoo 1.8-6 2019-05-28 [1] CRAN (R 3.6.1)
[1] /home/imallona/soft/R/R-3.6.1/library